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Sea Level Data Processing Workshop with SLPR2.4

Join the GLOSS Training Workshop in Japan, organized by the Meteorological Agency, from May 15-26, 2006. Learn about quality control of hourly sea level data using SLPR2.4 software. Topics covered include residuals, timing shifts, data gaps, spikes, and glitches. Gain practical experience in interpreting residual plots and implementing correction procedures.

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Sea Level Data Processing Workshop with SLPR2.4

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  1. GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 4. Quality Control of Hourly Data

  2. SLPR2 Quality Control Procedures Manual Section 5.1-5.4 Residuals = predicted tides minus observed data Use \slpr2\QC\RESID.EXE Plot with \slpr2\plot\HOURYR.EXE Three types of correctable errors • 1.Timing shifts (exact increments • of an hour) • Short gaps • 3. Data spikes, glitches

  3. SLPR2 Quality Control Procedures Manual Section 5.1-5.4 Interpretation of Residual Plots Foreman Tidal Analysis can not resolve all tidal constituents in some cases Complex shallow water tides

  4. SLPR2 Quality Control Procedures Example: Possible Timing Shift and Data Spikes

  5. SLPR2 Quality Control Procedures Look at Plot of Hourly Data for Clues

  6. SLPR2 Quality Control Procedures Same year of data, yet predicted tides based on different years of tidal analysis Balboa 1995 (based on 12/91-12/92) Balboa 1995 (based on 1/95 – 1/96)

  7. SLPR2 Quality Control Procedures Manual Section 5.3 Timing Errors • Causes: • Incorrect setting of initial gauge time • -Tide gauge clock error • -Error during digitization • -Error during transfer of data • -Programmer error in data file management

  8. SLPR2 Quality Control Procedures Manual Section 5.3 Timing Errors SLPR2 Correction Method Only possible for shifts of exact increments of an hour in hourly data files Step 1. Identify timing error in residual plot Step 2. Review original gauge data files to search for error (digitization table) Step 3. Estimate the magnitude and direction of shift -Use predicted tides file: See Appendix I Step 4. Determine the hour/day of the start/end (use residual data file) Step 5. run \slpr2\util\TSALL.EXE Step 6. Copy original file to a backup area, cut out bad segment in data file, paste in corrected segment from output of TSALL.EXE Step 7. Verify correction (make and plot residuals)

  9. SLPR2 Quality Control Procedures Manual Section 5.4 Short Gaps (24 hours long or less) Causes: • Instrument malfunction • Gauge clock malfunction • - Data transmission glitch Correction: Step 1. Use \slpr2\util\GAPCOU.EXE to get count of missing hours Step 2. Use \slpr2\qc\GAPFALL.BATp1 (p1 is station number) Must be ran from MS DOS Prompt window Step 3. Look at output file, GAPsss.TXT (sss: station number) Step 4. Make and plot residuals for interpolated file to verify correction Interpolation Method: Predicted Tide Method

  10. SLPR2 Quality Control Procedures Manual Section 5.4 Spikes and Glitches (24 hours long or less) Causes: • Instrument malfunction • Data transmission glitch • -Digitizing error Correction: Step 1. Identify spikes and glitches in residual plots Step 2. Using text editor (split screen with data file on top and residual file on bottom), to replace hour of spike (or consecutive hours with glitch) with 9999 (missing data flag) Step 4. run GAPFALL.BAT (as for short gaps) Step 3. Make and plot residuals to verify correction

  11. SLPR2 Quality Control Procedures Data File Management Place finalized, quality controlled, calibrated data in separate directory (up to individual agencies on how to define these) At least have the original (pre-corrections) file safely saved Evaluate whether to save intermediate processing step files (usually these are deleted)

  12. HOTS ASSIGNMENT • Make and plot residuals file for available years of data (at least one year) • If no timing errors are present, use the sample file under \slpr2\SAMP • Go through the steps for Timing Erorr Correction • Identify any spikes or glitches • Correct and verify • 6. Perform mock finalized data management

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